Modern vehicles are often equipped with a driver assistance system which supports the driver in driving the vehicle and facilitates the execution of certain driving maneuvers. To this end, the driver assistance system requires information about the vehicle's surroundings, this information being obtained via the position finding system, e.g., a radar system, a lidar system or a video camera system. The ACC system is an example of such a driver assistance system or a subsystem of such a driver assistance system, which is also capable of fulfilling additional functions. Details regarding such an ACC system are described, for example, in the publication “Adaptive Cruise Control—System Aspects and Development Trends” by Winner, Witte et al., published as SAE Technical Paper Series No. 961010 at SAE on Feb. 26 through 29, 1996. If the position finding system in an ACC system is based on a radar sensor, then not only the distances of the objects but the relative speeds of the objects are measurable directly with the help of the Doppler effect, so that by comparing these relative speeds with the absolute speed of the host vehicle, i.e., the vehicle equipped with the ACC system, it is possible to differentiate between lead vehicles and stationary objects, also called stationary targets, such as traffic signs, guardrail posts and the like.
For the distance regulating function, it is also necessary to differentiate between vehicles traveling in the host vehicle's lane and those traveling in neighboring lanes. This is possible because the radar system also has a certain angular resolution. The position of the located objects may then be obtained in a two-dimensional Cartesian coordinate system based on the distance data and angle data. In the case of a straight path of the road, it is thus possible to estimate relatively reliably whether a lead vehicle is in the host vehicle's lane or in a neighboring lane. In the case of a curved path of the road, however, the road curvature must also be taken into account in making this decision. Therefore, an important component function of the ACC system is to estimate the anticipated path of the road in a certain section ahead of the host vehicle. This function is known as travel course prediction.
In a known method for travel course prediction, the host vehicle movement data is analyzed. On the basis of the steering angle and/or the transverse acceleration or yaw rate of the vehicle measured by suitable sensors, the road curvature in the section of road on which the vehicle is currently traveling may be estimated in conjunction with the speed of the host vehicle. The travel course prediction is then based on the assumption that the road curvature will change slightly in the section of road directly ahead.
German Patent No. 197 20 764 describes a method of the type defined in the preamble, in which travel course prediction is based essentially on the position finding of stationary roadside targets. Although the relatively smooth road surface generally has only a low reflectivity for radar waves, there are frequently objects having a higher reflectivity on the roadside. These roadside objects may be spatially limited objects such as posts, traffic signs and the like which are identifiable and trackable using the known tracking methods and repeated radar measurements, but they may also be extensive objects such as guardrails, greenery, walls and the like, where such tracking is impossible. The publication cited above describes a method in which both types of roadside objects may be used to determine the path of the road. To this end, the tracking angle range of the radar is divided into discrete segments, and the stationary targets which are situated at or beyond the roadside are identified for each segment by comparing the threshold value of the amplitudes of the received radar echoes for each segment. A function which approximately reflects the shape of the roadside is then calculated by curve regression from the distance data for the roadside obtained for each angular segment.
In this method, a somewhat reliable value for the distance from the roadside must be determined for each angular segment, and to this end, radar echoes having a relatively small amplitude must also be analyzed. This results in complex processing and a relatively great susceptibility of the method to interference.
German Patent No. 197 22 947 describes another method for travel course prediction in which the main data used for travel course prediction is the tracking data on vehicles traveling ahead. However, an accurate travel course prediction is made difficult here by the fact that in the case of a wider road, e.g., a three-lane road, the lead vehicles have a relatively great lateral offset within the lane. It is proposed in the publication cited above that stationary targets should also be used for travel course prediction.
In general it is expedient to combine several different methods of travel course prediction and to adjust them mutually to thereby improve the accuracy and reliability of the travel course prediction.